code: def make_dataset():
      data, target = make_classification(n_samples=10000, n_features=20)
  
      # number of categorical columns
      n_categorical = 3
  
      # if value x in [0, 0.2) then category 0
      # if value x in [0.2, 0.5) then category 1
      # if value x in [0.5, 0.8) then category 2
      # if value x in [0.8, 0.1] then category 3
      bins = np.array([0, 0.2, 0.5, 0.8, 1])
  
      # Create categorical features
      for i in range(n_categorical):
          data[:, i] = np.digitize(data[:, i], bins=bins)
  
      return train_test_split(data, target, test_size=0.1)
  
file_path: neupy/examples/mlp/mix_categorical_numerical_inputs.py
imports: import numpy
  from sklearn.model_selection import train_test_split
  from sklearn.datasets import make_classification
  from sklearn.metrics import accuracy_score
  from neupy import layers, algorithms, utils
label_desc: Use image node_id 1 for calling a global function with example usage: make_dataset() and returns: train_test_split